Technical Field
The present invention relates to a question and answer (Q&A) database (QADB), and more specifically, to an expansion of the QADB.
Description of the Related Art
In contact centers of companies, it is required to answer to various questions from customers promptly for reducing cost and improving customer satisfaction. A call agent supporting system can transcribe conversations between a call agent and a customer using speech recognition system and search relevant and similar question and answer pairs from a database and, then, show the found pairs to the call agent.
In a case of Q&A search using a keyword search, such as Google® search, the Q&A system calculates similarity score of all Q&As and, then, returns a most relevant Q&A which has a highest similarity score.
For example, if the keywords for search are “address” and “change”, the Q&A system searches for a most relevant Q&A among a set of the Q&A documents. Let us suppose that the set of the Q&A documents includes the following Q&A documents:
“Q&AXXXX Address change, Name change: Have a WEB ID . . . Customer can change from the web, otherwise go to a branch with ID” and
“Q&AYYYY Online ID change: Call a free dial xxxx-xxxx. The caller must be an account owner. Prepare account number, hints”. The Q&A having the identifier number, Q&AXXXX, comprises the terms, “Address change”, and “Name change”. Accordingly, the Q&A system returns the Q&A having the identifier number, Q&AXXXX, as a most relevant Q&A.
When we search for Q&A utilizing speech recognition results, a most relevant Q&A document is searched among a set of Q&A documents, utilizing speech recognition results as inputs. However, there is a difference between vocabulary of the speech recognition results and contents of the set of Q&A documents, because spoken terms and written terms do not always overlap.
For example, the speech recognition results are as follows: “do you want to open an account for your daughter?”, “opening an account to eight year old boy . . . ”. The contents of the set of the Q&A documents are as follows: “Q&A ZZZZ Open account for under eighteen year-olds”. In the example mentioned above, the speech recognition results do not match the contents of the set of the Q&A documents, because representation manners for child is different from each other.
Accordingly, accuracy of the Q&A search is low, compared to a keyword search.
Speech recognition results are used to search for a new inquiry, which are not included in the existing Q&A set by classifying speech recognition results to existing Q&A (see Japanese Patent Laid-Open No. 2012/003704).
A query expansion is generally known as a method for preventing retrieval omission when a keyword does not match the contents of the set of the Q&A documents.